Here's a quick guide on how to perform sentiment analysis and other tasks using LangChain, LLM (Large Language Models), NLP (Natural Language Processing), and statistical analytics.
Sentiment Analysis with LangChain and LLM
- Install Required Libraries:
pip install langchain openai transformers - Set Up OpenAI API:
import openai openai.api_key = 'your_openai_api_key' - LangChain for Sentiment Analysis:
from langchain.llms import OpenAI from langchain import Chain # Initialize OpenAI LLM llm = OpenAI(model="text-davinci-003") # Define a function for sentiment analysis def analyze_sentiment(text): response = llm.completion( prompt=f"Analyze the sentiment of the following text: {text}", max_tokens=60 ) return response.choices[0].text.strip() # Example usage text = "I love the new design of the website!" sentiment = analyze_sentiment(text) print(f"Sentiment: {sentiment}") Additional NLP Tasks with LangChain and LLM
Text Summarization
def summarize_text(text): response = llm.completion( prompt=f"Summarize the following text: {text}", max_tokens=150 ) return response.choices[0].text.strip() # Example usage text = "Your detailed article or document here." summary = summarize_text(text) print(f"Summary: {summary}") Named Entity Recognition (NER)
def extract_entities(text): response = llm.completion( prompt=f"Extract the named entities from the following text: {text}", max_tokens=100 ) return response.choices[0].text.strip() # Example usage text = "OpenAI, founded in San Francisco, is a leading AI research institute." entities = extract_entities(text) print(f"Entities: {entities}") Statistical Analytics with NLP
Word Frequency Analysis
from collections import Counter import re def word_frequency_analysis(text): words = re.findall(r'\w+', text.lower()) frequency = Counter(words) return frequency # Example usage text = "This is a sample text with several words. This text is for testing." frequency = word_frequency_analysis(text) print(f"Word Frequency: {frequency}") Sentiment Score Aggregation
def sentiment_score(text): sentiment = analyze_sentiment(text) if "positive" in sentiment.lower(): return 1 elif "negative" in sentiment.lower(): return -1 else: return 0 # Example usage texts = ["I love this!", "This is bad.", "It's okay."] scores = [sentiment_score(t) for t in texts] average_score = sum(scores) / len(scores) print(f"Average Sentiment Score: {average_score}") For more advanced uses and customization, refer to the LangChain documentation and the OpenAI API documentation.
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